Data-driven feedback stabilization of nonlinear systems: Koopman-based model predictive controlElectrical Engineering and Systems Science - Systems and ControlIn this work, a predictive control framework is presented for feedback stabilization of nonlinear systems. To achieve this, we integrate Koopman ...
[2] Xie, Shengwen, and Juan Ren. "Linearization of Recurrent-neural-network-based models for Predictive Control of Nano-positioning Systems using Data-driven Koopman Operators" IEEE Access (2020). DOI:10.1109/ACCESS.2020.3013935. 4 Matlab代码、数据、文章 ...
In numerical examples, the linear predictors obtained in this way exhibit a performance superior to existing linear predictors such as those based on local linearization or the so called Carleman linearization. Importantly, the procedure to construct these linear predictors is completely data-driven and...
Model predictive controlNonlinear processIn this paper, we introduce a data-driven predictive control approach leveraging Koopman operator theory for addressing nonlinear dynamics within intricate systems. The methodology employs GSINDy algorithms to construct a repository of functions, which are subsequently ...
In numerical examples, the linear predictors obtained in this way exhibit a performance superior to existing linear predictors such as those based on local linearization or the so called Carleman linearization. Importantly, the procedure to construct these linear predictors is completely data-driven and...
the linear predictors obtained in this way exhibit a performance superior to existing linear predictors such as those based on local linearization or the so called Carleman linearization. Importantly, the procedure to construct these linear predictors is completely data-driven and extremely simple – it...
This paper deals with the issue of robust model predictive control (MPC)-based automated vehicle platooning control under acceleration constraints. A unifi... C Huang,Q Shi,W Ding,... - 《Control Engineering Practice》 被引量: 0发表: 2023年 Driver-centric data-driven robust model predictive ...
This approach is data driven, yet yields an explicit control-oriented model rather than just a "black-box" input鈥搊utput mapping. This work describes a Koopman-based system identification method and its application to model predictive control (MPC) design for soft robots. Three MPC controllers ...
Learning-based robust model predictive control with data-driven Koopman operators This paper presents a data-driven control strategy for nonlinear dynamical systems, which fully exploits the advantages of the Koopman operator in globally... M Wang,X Lou,B Cui - 《International Journal of Machine ...
In numerical examples, the linear predictors obtained in this way exhibit a performance superior to existing linear predictors such as those based on local linearization or the so called Carleman linearization. Importantly, the procedure to construct these linear predictors is completely data-driven and...